1. Technical Field
One or more embodiments described herein relate generally to expanding and improving search queries. More specifically, one or more embodiments relate to composing intelligent semantic-based expanded search queries and performing searches using such search queries.
2. Background and Relevant Art
Users perform numerous searches on a daily basis. For example, a user may perform Internet searches for restaurant reservations, retail goods, travel directions, business information, news, weather, and so forth. Users generally perform these Internet searches by utilizing one or more Internet search engines that receive a search query from the user and return various results (e.g., hyperlinks and brief descriptions) related to the search query.
Often, a search engine will return search results that include expanded terms. To illustrate, a user may enter a search query into a search engine such as, “best hamburger places in San Francisco.” The search engine may return not only search results for highly rated hamburger restaurants in San Francisco, but also other search results related to search terms that are expanded from the terms in the original search query. For example, the search engine may return additional search results including search results related to other types of restaurants and/or restaurants in other locations besides San Francisco.
Typically search engines identify these expanded search results by simply matching the original search query to a large database of historical queries. The search engine then uses this historical data to suggest the most likely and popular expanded results. This approach to identifying expanded search results is problematic in certain contexts. For example, the expanded terms returned by a typical search engine relying on historical data may not be directed at a domain in which the user is actually interested.
For instance, in the above example, the user may not have a car and thus has no interest in any restaurant outside of San Francisco. Accordingly, those expanded search results for restaurants outside of San Francisco are of nominal interest to the user. A user may try to avoid such search results, but the user generally has no way of editing or manipulating the search query beyond simply editing the text of the query. For example, the user may try different word choices within the search query in hopes of narrowing the search results to something that is truly useful to the user, but this approach often wastes the user's time and results in frustration for the user.
Furthermore, additional problems arise for users who are attempting to compose and execute search queries on a mobile device. For example, due to the limited display space on a smart phone or other mobile device, users frequently struggle to input search query terms and revise search queries that are not returning desired results. Additionally, traditional search query composition methods only allow for typed input from a user. For example, users often find it frustrating to type and re-type iterative search queries out using a standard keyboard. The process of typing and re-typing search queries is even more frustrating, time-consuming, and error-filled when the user is utilizing touch gestures on the small display of a mobile device.
Thus, there are several disadvantages to current methods for helpfully manipulating search queries.